Fraud detection is an increasingly necessary aspect of commerce. Transactions made with fraudulent cards or real cards which are used fraudulently account for billions of dollars of business losses each year.
While the scope of fraudulent activity is broad, it is believed that a small portion of the population is responsible for the majority of fraudulent activity. Accordingly, identifying the primary actors allows a significant amount of fraud to be avoided.
Accordingly, there is a need to identify devices which are associated with fraudulent activity to proactively address further fraudulent activities. Additionally, other devices which are associated with or have various features in common with devices associated with fraudulent activity may be monitored more closely than other devices in order to guard against fraudulent activity later on.
What is needed is a system for establishing identifiers for devices associated with fraudulent activity and leverage analysis of other devices to identify identifiers suggesting affiliation with devices associated with fraudulent activity.